Financial Signal Processing: A Self Calibrating Model

A Signal Processing Perspective of Financial Engineering

Financial engineering and electrical engineering are seemingly different areas that share strong underlying connections. Both areas rely on statistical analysis and modeling of systems; either modeling the financial markets or modeling, say, wireless communication channels. Having a model of reality allows us to make predictions and to optimize the strategies. It is as important to optimize our...

A Multigrid Strategy for Calibrating Financial Models

For asset pricing purposes, ‘calibrating’ a model means to choose unobservable input parameters so that some output of the model matches some observed data. This practice is ubiquitous in financial engineering, financial economics, and macroeconomics, where an increasing number of models are being developed that must be solved numerically. It is for such models that we introduce a numerical met...

Financial Applications in Signal Processing Curricula

Modern signal processing heavily relies on probability and statistics. Naturally, these subjects are emphasized in the signal processing curricula, at both the undergraduate and graduate levels. Historically, however, both probability and statistics were developed in order to solve various applied problems arising in economics and finance. Specifically, probability theory owes its existence to ...

Financial Signal Processing and Machine Learning

A self-calibrating chrominance model applied to skin color detection

In case of the absence of a calibration procedure, or when there exists a color difference between direct and ambient light, standard chrominance models are not completely brightness invariant. Therefore, they cannot provide the best space for robust color modeling. Instead of using a fixed chrominance model, our method estimates the actual dependency between color appearance and brightness. Th...